Infrared (IR) images are capable of showing hidden objects in an environment where image quality is low. Visible images have high resolution and good image quality but cannot show hidden objects. Image fusion attempts to combine information content from multiple images to obtain a fused image with IR object features from the IR image and retain the visual details provided by the image. Hence

the fused image has the advantages of visible and IR images and is suitable for subsequent processing tasks. A new image fusion method based on nonsubsampled contourlet contourlet transform( NSCT ) and compressed sensing is presented for visual image and IR image. First

the IR and visual images are transformed by NSCT to obtain a low frequency sub-band and a series of high sub-bands with diverse scales and directions. Second

high-frequency sub-bands are fused with the rule of the maximum of local energy. The low-frequency sub-band is measured by a scrambled matrix to produce an observed vector

and the observed vectors are fused by the rule of the maximum of standard deviation. Finally

the fusion image is obtained by the inverse NSCT. Different fusion rules based on NSCT are simulated to compare with the new fusion method. Quantitative analysis is conducted for the fused image under parameters such as entropy

spatial frequency

standard deviation

and structural similarity. Experimental results show that the new proposed method can provide better fusion than other fusion methods in terms of subjective visual quality and objective fusion metric values. The proposed method can also preserve the details of the visible light image and the legible target of the IR image. The fused image enables the extraction of the target location for easy observation and provides information for the further processing of tasks. This image can be extensively used in many fields such as target detection

target localization

remote sensing

and robotic vision.